Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images
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Abstract (arXiv)
Brain tumors are among the deadliest diseases in the world. Magnetic Resonance Imaging (MRI) is one of the most effective ways to detect brain tumors. Accurate detection of brain tumors based on MRI scans is critical, as it can potentially save many lives and facilitate better decision-making at the early stages of the disease. Within our paper, four different types of MRI-based images have been collected from the database: glioma tumor, no tumor, pituitary tumor, and meningioma tumor. Our study focuses on making predictions for brain tumor classification. Five models, including four pre-trained models (MobileNet, EfficientNet-B0, ResNet-18, and VGG16) and one new model, MobileNet-BT, have been proposed for this study.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 1 Aug 2024.
Builds on
20 of 32 references.
References · 20
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Citing papers per year: 2024: 6; 2025: 1.
7 papers.
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- 2025ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connectionscited by 0
- 2025ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections24 Aug 2025
- 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models25 Oct 2024
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- 2024Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism20 Sep 2024
- 2024Comprehensive Overview of Artificial Intelligence Applications in Modern Industries19 Sep 2024
- 2024Enhancing Skin Lesion Diagnosis with Ensemble Learning6 Sep 2024
- 2024Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach13 Aug 2024